Proteomic insights into the pathophysiology of hypertension-associated albuminuria: Pilot study in a South African cohort

• Published in: Clinical proteomics 2024-02, Vol.21 (1), p.15-15, Article 15
• Main Authors: Govender, Melanie A, Stoychev, Stoyan H, Brandenburg, Jean-Tristan, Ramsay, Michèle, Fabian, June, Govender, Ireshyn S
• Format: Article
• Language: English
• Subjects: Albumin; Angiotensinogen; Apolipoproteins; Biomarkers; Blood pressure; Chromatography; Chronic kidney failure; Comparative analysis; Creatinine; DNA methylation; Epidemiology; Ethnicity; Extracellular matrix; High performance liquid chromatography; Hypertension; Immune system; Innate immunity; Kidney diseases; Kidneys; Learning algorithms; Liquid chromatography; Machine learning; Mass spectrometry; Mass spectroscopy; Matrix metalloproteinase; Peptides; Pilot projects; Proteins; Proteomics; Public health; Risk factors; Scientific imaging; Urine
• ISSN: 1542-6416; 1559-0275
• DOI: 10.1186/s12014-024-09458-9

Hypertension is an important public health priority with a high prevalence in Africa. It is also an independent risk factor for kidney outcomes. We aimed to identify potential proteins and pathways involved in hypertension-associated albuminuria by assessing urinary proteomic profiles in black South African participants with combined hypertension and albuminuria compared to those who have neither condition. The study included 24 South African cases with both hypertension and albuminuria and 49 control participants who had neither condition. Protein was extracted from urine samples and analysed using ultra-high-performance liquid chromatography coupled with mass spectrometry. Data were generated using data-independent acquisition (DIA) and processed using Spectronaut™ 15. Statistical and functional data annotation were performed on Perseus and Cytoscape to identify and annotate differentially abundant proteins. Machine learning was applied to the dataset using the OmicLearn platform. Overall, a mean of 1,225 and 915 proteins were quantified in the control and case groups, respectively. Three hundred and thirty-two differentially abundant proteins were constructed into a network. Pathways associated with these differentially abundant proteins included the immune system (q-value [false discovery rate] = 1.4 × 10 ), innate immune system (q = 1.1 × 10 ), extracellular matrix (ECM) organisation (q = 0.03) and activation of matrix metalloproteinases (q = 0.04). Proteins with high disease scores (76-100% confidence) for both hypertension and chronic kidney disease included angiotensinogen (AGT), albumin (ALB), apolipoprotein L1 (APOL1), and uromodulin (UMOD). A machine learning approach was able to identify a set of 20 proteins, differentiating between cases and controls. The urinary proteomic data combined with the machine learning approach was able to classify disease status and identify proteins and pathways associated with hypertension-associated albuminuria.